A Skew Detection Algorithm for PDF417 in Complex Background

Conference paper
First Online:
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 135)

Abstract

The automatic identification of 2D (two dimensional) bar code PDF417 is very sensitive to skew angle. However, the common skew angle detection methods have shortcomings such as weak performance in time complexity. This paper mainly introduces an algorithm that utilizes Mathematics Morphology to extract the PDF417 code area from the complex background and then get skew angle of PDF417 bar code image using the least square method based on the properties of PDF417 character code and the extraction of feature points. Experiments show that this algorithm has virtue of less computation and high accuracy.

Keywords

PDF417  Skew angel  Least square method  
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References

  1. 1.
    State Standard of the People’s Republic of China GB/T17172—1997, China Standard Press, 1998.8Google Scholar
  2. 2.
    Gatos B, Perantonis SJ, Papamarkos N (1996) Accelerated Hough transform using rectangular image decomposition. Electron Lett 32(8):730–732CrossRefGoogle Scholar
  3. 3.
    Ohbuchi E, Hanaizumi H, Hock LA (2004) Barcode readers using the camera device in mobile phones. In: Proceedings of the 2004 international conference on cyber worlds (CW’04), pp 260–265Google Scholar
  4. 4.
    Smith R (1995) A simple and efficient skew detection algorithm via text row accumulation. Proceedings of third international conference on document analysis and recognition. IEEE, Montreal, pp 1145–1148Google Scholar
  5. 5.
    Hinds SC, Fisher JL (1990) A document skew detection method using run-length encoding and the hough transform. In: Proceedings of tenth international conference on pattern recognition. IEEE Computer Society, Atlantic City, pp 464–468Google Scholar
  6. 6.
    Shih FY, Pu CC (1995) Analysis of the properties of soft morphological filtering using threshold decomposition. IEEE Trans Signal Process 43(2):539–544 Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Jian-Hua Li
    • 1
  • Ping Li
    • 1
  • Yi-Wen Wang
    • 1
  • Xiao-Dan Li
    • 2
  1. 1.Key Laboratory of Electronic Thin Films and Integrated DevicesUniversity of Electronic Science and Technology of ChinaChengduChina
  2. 2.College of Computer Science and TechnologySouthwest University for NationalitiesChengduChina

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